Google’s DeepMind laboratory is currently working on a cutting-edge AI system called Gemini that is expected to compete with, or even surpass, the capabilities of ChatGPT, as reported by Wired.
To achieve this, the developers have planned to incorporate an older AI program known as AlphaGo into the upcoming language learning model (LLM). What sets AlphaGo apart is its use of reinforcement learning, a technique that allows the software to tackle complex problems through trial and error. By making repeated attempts and learning from failures, the AI can significantly improve its performance. The goal of DeepMind is to equip Google’s future LLM with the ability to solve complex problems, if not plan.
When combined with a generative AI’s ability to gather information from the internet and generate text that sounds natural, Gemini has the potential to surpass all other existing artificial intelligences in terms of intelligence. DeepMind’s co-founder and CEO, Demis Hassabis, even claims that if executed correctly, Gemini could be the most beneficial technology for humanity ever created. These are certainly bold words.
The development process for Gemini is currently underway and is expected to take several months, according to Hassabis. Additionally, the project comes with a significant cost for Google, ranging from tens to hundreds of millions of dollars. In comparison, ChatGPT alone cost over $100 million to develop.
Analysis: Is It Too Good to Be True?
While Gemini sounds intriguing, it is important to remain skeptical at this stage. Our main concern lies with AlphaGo itself.
AlphaGo gained recognition back in 2016 when it defeated a champion player in the complex board game Go, known for its deceptive simplicity. The AI’s success in this game was mainly due to its reinforcement learning technique, allowing it to explore and remember all possible moves.
However, how does AlphaGo’s expertise in a board game translate to solving complex problems or generating content? Proficiency in one specific scenario does not guarantee similar performance in another field. Furthermore, is it wise to rely on a generative AI to trial and error its way to finding solutions? AI hallucinations are already causing concerns, and while AlphaGo can aid Gemini in faster improvement, the potential risks and challenges must not be ignored.
Moreover, the statement suggesting that development will be completed within a few months raises concerns. Earlier this year, Google faced criticism after rapidly launching its AI-powered chatbot, Bard, following the success of ChatGPT. Bard was heavily criticized for providing misleading information and labeled as “worse than useless.” Perhaps it would be prudent for Google or DeepMind to extend the development cycle from months to years and provide more training to Gemini. After all, what’s the rush?
In the meantime, feel free to explore TechRadar’s updated list of the best AI writers for 2023.
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Deepak Sen is a tech enthusiast who covers the latest technological innovations, from AI to consumer gadgets. His articles provide readers with a glimpse into the ever-evolving world of technology.
